基于全球优化和分类问题的等位数相似性的随机粒子群优化.
Yujia Liu1, Yuan Zeng1, Rui Li2
1School of Intelligent Manufacturing Engineering, Jiangxi College of Application Science and Technology, Nanchang 330000, China.
一个新的随机粒子优化 (RPSO) 算法增强了全球优化. RPSO提高了复杂问题的搜索效率和准确性,包括卷积神经网络 (CNN) 分类.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 机器学习 机器学习
背景情况:
- 优化问题的日益复杂性需要具有卓越的全球优化能力的算法.
- 传统的粒子集群优化 (PSO) 在有效探索广的搜索空间和实现精确的局部优化方面面临挑战.
- 开发先进的优化技术对于各种科学和工程应用至关重要.
研究的目的:
- 引入一种新的优化算法,即随机粒子群优化 (RPSO),旨在增强全球优化.
- 在公共服务任务框架内改善勘探和开采平衡.
- 验证RPSO在基准数据集和现实世界应用程序 (如卷积神经网络 (CNN) 分类) 上的性能.
主要方法:
- 通过通过调整参数选择和随机对比相互作用 (RCI) 机制来增强传统的PSO,开发了RPSO.
- 整合了二次插值 (QI) 来提高本地搜索效率.
- 在RCI和QI选择中,用于动态人口信息更新的共弦相似性.
主要成果:
- 在2022年IEEE进化计算大会 (CEC) 测试数据集上,RPSO表现出强大的竞争力,与最先进的算法相对抗.
- 该算法显示了全球优化任务性能的显著改进.
- 在基于CNN的医学图像分类中,RPSO实现了与现有方法相比的稳定性和准确性.
结论:
- RPSO提供了一种有效的方法来解决复杂的全球优化问题.
- 在RPSO中的改进,包括RCI和QI,导致更有效的搜索和更好的解决方案质量.
- 对于提高机器学习模型的性能,RPSO显示出有前途,特别是在CNN分类任务中.
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